In computer vision systems that seek to reconstruct a three-dimensional representation of a scene or object from two-dimensional images of the scene or object, important parameters are the changes in viewpoints of the different views of the scene. When two images of the scene represent two views that involve unknown rotation and translation of the camera recording the scene, to be termed egomotion, such as might result from noise, considerable computation is involved in making a faithful three-dimensional reconstruction. A faithful three-dimensional reconstruction has utility in many applications, such as estimation of travel in navigation, three-dimensional representation of an object from two two-dimensional representations and video mosaicing, the integration of many views of different parts of a scene into a single view of the total scene, such as is described in an article by R. Kumar et al entitled, "Shape recovery from multiple views: a parallax based approach," in the Proc. of ARAP Image Understanding Workshop, 1994.
The problem of estimating the ego-motion and structural form from two image frames of a scene has long been studied in computer vision. There have been primarily two distinct classes of structure-and-motion algorithms that have been tried. The first is feature-based and assumes that there is a known number of feature-correspondence between the two frames. While few correspondences are needed in theory to solve the structure-and-motion problem, this approach is very sensitive to noise and many correspondences are in fact needed to stabilize the solution. Moreover, it is often the case that no feature-correspondences are known a priori and finding these can be laborious.
The second approach involves a class of direct methods of motion-and-structure estimating in which explicit feature-correspondences are not required.
Solutions using this approach can be broadly categorized into two main subclasses. One subclass approach to the problem is first to develop knowledge of the optical flow field of the frames involved. The second subclass approach has been to exploit the brightness-change constraint equation directly to develop solutions for motion and structure, as is described in an article by B. K. P. Horne and E. J. Weldon, Jr. entitled, "Direct Methods for Recovering Motion," in Int. J. of Computer Vision, vol. 2, 1988, pages 51-76.